Recently, self-supervised transformers (SSTs) [1]} has attracted much attention for WSOL tasks. Using only self-supervision, these models can yield impressive saliency maps. Given their long-range receptive fields, SSTs analyze an entire input image, allowing to build saliency maps that cover full objects. Transformers are able to identify multiple objects by decomposing the scene into different spatial saliency maps. Such localization information is accumulated in maps, i.e. class tokens, at the top layer (see Fig. REF (b)). However, without any class supervision, these tokens are arbitrary and class-agnostic, and objects of interest cannot be dissociated from others. Each token focuses on a random object with semantics that differ from one image to another, making them less reliable for localization. Unless ground truth localizations are provided to select the best token [1]}, their application in WSOL remains limited.
